Exploit for Origin Validation Error in Ultraviolet Cocos_Ai

DugganUSA — IETF Hackathon Contributions Real-world threat-in...

Veritas: A Semantically Grounded Agentic Framework for Memory Corruption Vulnerability Detection in Binaries

Detecting memory corruption vulnerabilities in stripped binaries requires recovering object semantics, interprocedural propagation, and feasible triggers from low-level, lossy representations. Recent LLM-based approaches improve code understanding, but reliable detection still requires grounding ...

Malicious code in @uipath/context-grounding-tool (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 08219b377dcb6cc4d5e37e03ac84d8fbce414fc1388eda8d60092c4f468c3cac Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

MAL-2026-3541 Malicious code in @uipath/context-grounding-tool (npm)

--- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 08219b377dcb6cc4d5e37e03ac84d8fbce414fc1388eda8d60092c4f468c3cac Any computer that has this package installed or running should be considered fully compromised. All secrets and keys stored on that computer should be...

Threat Modelling Using Domain-Adapted Language Models: Empirical Evaluation and Insights

Large Language ModelsLLMs are increasingly explored for cybersecurity applications such as vulnerability detection. In the domain of threat modelling, prior work has primarily evaluated a number of general-purpose Large Language Models under limited prompting settings. In this study, we extend th...

LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution

LLMs are increasingly explored for malware analysis; however, current LLM-based malware attribution remains limited by unsupported indicators and insufficient code-level grounding for identifying malicious and vulnerable code segments. To address these limitations, this research introduces LCC-LL...

Beyond RAG for Cyber Threat Intelligence: A Systematic Evaluation of Graph-Based and Agentic Retrieval

Cyber threat intelligence CTI analysts must answer complex questions over large collections of narrative security reports. Retrieval-augmented generation RAG systems help language models access external knowledge, but traditional vector retrieval often struggles with queries that require reasonin...

Red-Teaming Claude Opus and ChatGPT-Based Security Advisors for Trusted Execution Environments

Trusted Execution Environments TEEs e.g., Intel SGX and ArmTrustZone aim to protect sensitive computation from a compromised operating system, yet real deployments remain vulnerable to microarchitectural leakage, side-channel attacks, and fault injection. In parallel, security teams increasingly...

LLM-Assisted Authentication and Fraud Detection

User authentication and fraud detection face growing challenges as digital systems expand and adversaries adopt increasingly sophisticated tactics. Traditional knowledge-based authentication remains rigid, requiring exact word-for-word string matches that fail to accommodate natural human memory...

A Prompt-Based Framework for Loop Vulnerability Detection Using Local LLMs

Loop vulnerabilities are one major risky construct in software development. They can easily lead to infinite loops or executions, exhaust resources, or introduce logical errors that degrade performance and compromise security. The problem are often undetected by traditional static analyzers becau...

ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst...

Evaluation of Vision-LLMs in Surveillance Video

The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective...

Toward Cybersecurity-Expert Small Language Models

Large language models LLMs are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domain-specific models and training datasets. To address this gap, we present CyberPal 2.0, a family of cybersecurity-expert small language models SLMs ranging fr...

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities

Traditional Artificial Intelligence AI approaches in cybersecurity exhibit fundamental limitations: inadequate conceptual grounding leading to non-robustness against novel attacks; limited instructibility impeding analyst-guided adaptation; and misalignment with cybersecurity objectives...

IAG: Input-Aware Backdoor Attack on VLMs for Visual Grounding

Vision-language models VLMs have shown significant advancements in tasks such as visual grounding, where they localize specific objects in images based on natural language queries and images. However, security issues in visual grounding tasks for VLMs remain underexplored, especially in the conte...

Dated, Vulnerable, Insecure Tech Is All Over the News. Hooray.

Save the links. Pass them around. And consider getting your copy of the new 2023 XDR Buyer’s Guide—because if this isn’t a time for reckoning and progress, what is? The news: on Wednesday, the United States grounded all flights coast-to-coast for the first time since 9/11. The Federal Aviation...